This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antibiotic results are from our example_isolates data set. All patient names are created using online surname generators and are only in place for practice purposes.

WHONET

## Format

A data.frame with 500 observations and 53 variables:

• Identification number
ID of the sample

• Specimen number
ID of the specimen

• Organism
Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using as.mo().

• Country
Country of origin

• Laboratory
Name of laboratory

• Last name
Fictitious last name of patient

• First name
Fictitious initial of patient

• Sex
Fictitious gender of patient

• Age
Fictitious age of patient

• Age category
Age group, can also be looked up using age_groups()

• Date of admission

• Specimen date
Date when specimen was received at laboratory

• Specimen type
Specimen type or group

• Specimen type (Numeric)
Translation of "Specimen type"

• Reason
Reason of request with Differential Diagnosis

• Isolate number
ID of isolate

• Organism type
Type of microorganism, can also be looked up using mo_type()

• Serotype
Serotype of microorganism

• Beta-lactamase
Microorganism produces beta-lactamase?

• ESBL
Microorganism produces extended spectrum beta-lactamase?

• Carbapenemase
Microorganism produces carbapenemase?

• MRSA screening test
Microorganism is possible MRSA?

• Inducible clindamycin resistance
Clindamycin can be induced?

• Comment

• Date of data entry
Date this data was entered in WHONET

• AMP_ND10:CIP_EE
28 different antibiotics. You can lookup the abbreviations in the antibiotics data set, or use e.g. ab_name("AMP") to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using as.rsi().

## Reference data publicly available

All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.

## Read more on our website!

On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!